Description

This file is mainly focus on the prelimianry selected sites by Beni that do not have the early GPP estimation

step1: tidy the table for GPP simulation vs GPP obs sites

step2: adopt the same way to separate out the model early simulation period as for the sites with early GPP estimation

step1: tidy the table

library(kableExtra)
library("readxl")
table.path<-"C:/Users/yluo/Desktop/CES/Data_for_use/"
my_data <- read_excel(paste0(table.path,"Info_Table_about_Photocold_project.xlsx"), sheet = "Sites_without_earlyGPPest")
# my_data %>%
# kbl(caption = "Summary of sites with early GPP estimation") %>%
#   kable_paper(full_width = F, html_font = "Cambria") %>%
#   scroll_box(width = "500px", height = "200px") #with a scroll bars
my_data %>%
  kbl(caption = "Summary of sites with early GPP estimation") %>%
  kable_classic(full_width = F, html_font = "Cambria")
Summary of sites with early GPP estimation
SiteName Delay_status Long. Lat. Period PFT Clim. N Calib. Avai.analyzed.years-spring Avai.site-years-spring Avai.analyzed.years-springawinter Avai.site-years-springawinter Reference
IT-Ren No 11.43 46.59 1998-2013 ENF Dfc 3405 Y 2002-2003,2005-2013 11 2002-2003,2005-2013 11 Montagnani et al. (2009)
RU-Ha1 No 90.00 54.73 2002-2004 GRA Dfc 567 NA no early years (2002-2004 lack early doy) 0 no early years (2002-2004 lack early doy) 0 Belelli Marchesini et al. (2007)
BE-Vie No 6.00 50.31 1996-2014 MF Cfb 4910 Y 2000-2014 15 2000-2014 15 Aubinet et al. (2001)
CH-Cha No 8.41 47.21 2005-2014 GRA Cfb 2944 NA 2006-2008,2010-2014 8 2006-2008,2010-2014 8 Merbold et al. (2014)
CH-Lae No 8.37 47.48 2004-2014 MF Cfb 3551 Y 2005-2014(2004 lack early doy) 10 2005-2014(2004 lack early doy) 10 Etzold et al. (2011)
CH-Oe1 No 7.73 47.29 2002-2008 GRA Cfb 2184 Y 2002-2008 7 2002-2008 7 Ammann et al. (2009)
DE-Gri No 13.51 50.95 2004-2014 GRA Cfb 3642 Y 2004-2014 11 2004-2014 11 Prescher et al. (2010)
DE-Obe No 13.72 50.78 2008-2014 ENF Cfb 2260 Y 2008-2014 7 2008-2014 7 NA
DE-RuR No 6.30 50.62 2011-2014 GRA Cfb 1227 Y 2012-2014 3 2012-2014 3 Post et al. (2015)
DE-Tha No 13.57 50.96 1996-2014 ENF Cfb 5141 Y 2000-2014 15 2000-2014 15 Grünwald and Bernhofer (2007)
NL-Hor No 5.07 52.24 2004-2011 GRA Cfb 2188 Y 2005,2007-2011 6 2005,2007-2010 5 Jacobs et al. (2007)
NL-Loo No 5.74 52.17 1996-2013 ENF Cfb 4671 Y 2000-2013 14 2000-2013 14 Moors (2012)
Sum NA NA NA NA NA NA 36690 NA NA 107 NA 106 NA

step2: seprate the time period when model early estimation of GPP

Part1: find the method to determine the period that with early GPP estimation

Part 2: check all the sites

    1. For Dfc:for ENF
## [1] 5

(2) For Cfb:for GRA, MF and ENF sites

  • Cfb-GRA (5 site)
## [1] 8

## [1] 7

## [1] 11

## [1] 3

## [1] 6

- Cfb-ENF (3 site)

## [1] 7

## [1] 15

## [1] 14

```

step3: save the data that label with “is_event”

Summary

steps to determine the “is_event” period

Step1: normlization for all the years in one site

#normalized the gpp_obs and gpp_mod using the gpp_max(95 percentile of gpp)

Step 2:Determine the green-up period for each year(using spline smoothed values):

#followed analysis is based on the normlized “GPP_mod”time series(determine earlier sos)

  • using the normalized GPP_mod to determine sos,eos and peak of the time series (using the threshold, percentile 10 of amplitude, to determine the sos and eos in this study). We selected the GPP_mod to determine the phenophases as genearlly we can get earlier sos compared to GPP_obs–> we can have larger analysis period

    Step 3:rolling mean of GPPobs and GPPmod for data for all the years(moving windown:5,7,10, 15, 20days)

    also for the data beyond green-up period–> the code of this steps moves to second step

    • at the end, I select the 20 days windows for the rolling mean

    Step 4:Fit the Guassian norm distribution for residuals beyond the green-up period

    • The reason to conduct this are: we assume in general the P-model assume the GPP well outside the green-up period (compared to the observation data).

    • But in practise, the model performance is not always good beyond the green-up period–>I tested three data range:

      1. [peak,265/366]

      2. DoY[1, sos]& DOY[peak,365/366]

      3. [1,sos] & [eos,365/366]

    I found the using the data range c, the distrbution of biase (GPP_mod - GPP_obs) is more close to the norm distribution, hence at end of I used the data range c to build the distribution.

    step 5:determine the “is_event” within green-up period

    • After some time of consideration, I took following crition to determine the “is_event”:

      1. during the growing season period (sos,eos)–>the data with GPP biases bigger than 1.2 SD are classified as the “GPP overestimation points”

      2. For “GPP overstimation points”, thoses are air temparture is less than 10 degrees will be classified as the “is_event”. I selected 10 degree as the crition by referring to the paper Duffy et al., 2021 and many papers which demonstrate the temperature response curve normally from 10 degree (for instance: Lin et al., 2012)

      References:

      Duffy et al., 2021:https://advances.sciencemag.org/content/7/3/eaay1052

      Lin et al., 2012:https://academic.oup.com/treephys/article/32/2/219/1657108

    step 6:Evaluation “is_event”–>visualization and stats

    • two ways to evaluate if “is_event” is properly determined:

    1. visulization

    2. stats: \[ Pfalse = \frac{days(real_{(is-event)})}{days(flagged_{(is-event)})} \]